Intelligent Camera Analytics for Security and Operations

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

2016-01-01 (day precision)Source: 01/01/2016 00:00:00

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

Summarization, Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

Traditional video surveillance requires continuous human monitoring, which is resource-intensive and prone to missed events. By applying AI-driven camera analytics, this use case addresses the need for faster, more reliable detection of security incidents and operational insights. The purpose is to enhance situational awareness, reduce manual effort, and improve response times while also enabling organizations to repurpose video data for business and safety improvements.

Expected benefits

By leveraging AI-driven video surveillance, organizations can significantly improve efficiency and security outcomes. Automated monitoring reduces the need for constant human oversight, lowering operational costs while also decreasing the likelihood of missed incidents. Faster and more accurate detection of anomalies—such as unauthorized access, unattended objects, or unusual behavior—enhances safety for personnel and protects critical assets. In addition, video analytics provide valuable secondary benefits, including occupancy tracking, traffic flow analysis, and facility utilization insights that support data-driven decision-making. These capabilities enable scalability without requiring proportional increases in staffing, delivering measurable return on investment through reduced false alarms, quicker response times, improved compliance with safety protocols, and optimized use of resources.

System outputs

The AI system generates real-time outputs such as object detections, activity classifications, and anomaly alerts. These may include identifying people, vehicles, or objects of interest; detecting unusual behaviors like loitering or perimeter breaches; and flagging safety or security incidents for operator review. The system can also produce analytics dashboards and reports, summarizing trends such as occupancy levels, traffic flow, and space utilization, which support both security response and operational decision-making.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

Enterprise Physical Access Control System

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

Milestone, Genetec, Verkada, Lenel

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

Not reported

Training and evaluation data

Since this use case leverages commercial off-the-shelf products, training and fine-tuning of the AI models are performed by the vendors themselves. Milestone, Genetec, and Verkada typically rely on large, vendor-curated datasets of video footage that include a wide range of environments, objects, behaviors, and lighting conditions. These datasets are used to train computer vision models for object detection, classification, activity recognition, and anomaly detection. Vendors also apply continuous model evaluation and updates using customer feedback and aggregated, anonymized performance data to improve accuracy, reduce false positives, and adapt to evolving security scenarios. Our role is focused on deployment and operational use of these capabilities rather than direct model training.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported